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techniquebased around the study of collective behavior in decentralized,self-organized systems.

Introduced by Beni & Wang in 1989.

Typically made up of a population of simple agents.

Examples in nature : ant colonies, bird flocking, animal herdingetc.

Intelligent Agents

Anagentis anything that can be viewed asperceivingitsenvironment throughsensorsandactingupon thatenvironment througheffectors.

Rational Agents

Rationality-

expectedsuccessgiven what has been perceived.

Rationality is not omniscience.

Ideal rational agent should do whatever action is expected tomaximize its performance measure, on the basis of theevidence provided by the percept sequence and whateverbuilt-in knowledgethe agent has.

Factors on which Rationality depends

Performance measure

(degree of success).

Percept sequence

(everything agent has perceived so far).

Agents knowledge

about the environment.

Actions

that agent can perform.

Structure of IA

Agent = Program + Architecture

A Simple Agent Program.

Simple Reflex Agents

FollowsCondition-Action Rule.

Needs to perceive its environment completely.

Model Based Agents

Need not perceive the environment completely.

Maintains aninternal state.

Internal states should be updated.

Goal Based Agents

Makes decisions to achieve a goal.

More flexible.

Utility Based Agents

A complete specification of the utility function allows rationaldecisions in two kinds of cases.

Many goals, none can be achieved with certainty.

Conflicting goals.

Environment

Accessible vs. Inaccessible

Deterministic vs. Non-deterministic

Episodic vs. Non-episodic

Static vs. Dynamic

Continuous vs. Discreet

An Environment Procedure

Ant Colony Optimization (ACO)

First ACO system-

Marco Dorgo,1992

Ants search for food.

The shorter the path the greater the pheromone left by an ant.

The probability of taking a route is directly proportional to thelevel of pheromone on that route.

As more and more ants take the shorter path, the pheromonelevel increases.